In the case of multimedia-based services delivery over heterogeneous environments, the challenge is to provide a consistent and uniform Quality of Experience (QoE) to the end user, while utilizing the available network resources efficiently. The QoE may be affected by a number of factors such as available bandwidth, variable link quality, network congestion, device resolution and screen size and so on. The user expectation is to be able to access multimedia services over any device and across heterogeneous network.
Presently, there are multiple technologies that support dynamic adaptation. All these techniques are vendor specific implementations that use limited context information to trigger adaptation of content. For example, some techniques may use buffering rate to design the adaptation rules, some other techniques may use current bandwidth, content metadata and buffering rate calculation for adaptation. For web content, most platforms provide the feature of selecting current network connection type (normal or low bandwidth). Based on this, the back-end serves pages with appropriate rich media content. It is not currently feasible to support features like dynamic selection and rendering of rich media based on current network state.
The drawbacks of the present technologies include server centric approach and not much information is available from the client side to decide on when to adapt. The Adaptation algorithms vary greatly in their interpretation of the network conditions leading to widely varying end user experience. They take only available bandwidth as context without integrating other contexts while in reality any change in the environment may act as trigger, not just the bandwidth. The Use of limited context (e.g. bandwidth estimates, buffering rate) results in sub-optimal adaptation decisions, for example the approach to be taken for congestion in wireless network would be different from congestion in the core network. The former can be addressed by varying rate at which content is fetched, while the latter may require adaptation of the content.
In view of the foregoing discussion, there is a need for an adaptation model which is not vendor specific, able to use multitude of contexts (e.g. device context, user context, location, latency etc.) and not restricted to any particular platform and deployment environment.